A continuation of Introduction to Probability & Statistics I, the
course emphasizes the theory of inferential statistics and its applications.
The Central Limit Theorem is more fully developed as are the concepts of
estimation and hypothesis testing. The properties of estimators are covered
and tests using normal, t, chi-square, and F distributions are studied.
Nonparametric methods, regression, and correlation are also covered.
Statistical software and/or graphing calculators will be used.

OBJECTIVES:

Students will understand the Central Limit Theorem from both an experimental
and theoretical point of view and will know the value of this theorem in
inferential statistics. They will know the desirable qualities for an estimator
and learn a number of techniques for finding minimum-variance, unbiased
estimators. They will know the elements of an hypothesis
test and be able to carry out a number of different hypothesis tests. They
will also learn about linear models and estimation by least squares.

2.1 Properties of point estimators
2.2 Evaluating the goodness of point estimators
2.3 Method of Maximum Likelihood
2.4 Confidence intervals for large samples
2.5 Confidence intervals for small samples
2.6 Confidence intervals for two samples

3. Hypothesis Testing

3.1 Elements of Statistical Test
3.2 Common large sample tests
3.3 Attained significance levels or
p-Values
3.4 Tests using the t distribution
3.5 Tests using the F distribution
3.6 Power of tests